Ferraro Elena, De Michielis Marco
CNR-IMM Agrate Unit, Via C. Olivetti 2, 20864, Agrate Brianza, MB, Italy.
Sci Rep. 2020 Oct 20;10(1):17780. doi: 10.1038/s41598-020-74817-z.
One of the main challenges in building a quantum processor is to characterize the environmental noise. Noise characterization can be achieved by exploiting different techniques, such as randomization where several sequences of random quantum gates are applied to the qubit under test to derive statistical characteristics about the affecting noises. A scalable and robust algorithm able to benchmark the full set of Clifford gates using randomization techniques is called randomized benchmarking. In this study, we simulated randomized benchmarking protocols in a semiconducting all-electrical three-electron double-quantum dot qubit, i.e. hybrid qubit, under different error models, that include quasi-static Gaussian and the more realistic 1/f noise model, for the input controls. The average error of specific quantum computational gates is extracted through interleaved randomized benchmarking obtained including Clifford gates between the gate of interest. It provides an estimate of the fidelity as well as theoretical bounds for the average error of the gate under test.
构建量子处理器的主要挑战之一是表征环境噪声。噪声表征可以通过利用不同技术来实现，例如随机化，即对被测量子比特应用几个随机量子门序列，以得出有关影响噪声的统计特性。一种能够使用随机化技术对全套克利福德门进行基准测试的可扩展且稳健的算法称为随机基准测试。在本研究中，我们在不同误差模型下，即包括准静态高斯噪声和更实际的1/f噪声模型，对输入控制的情况下，在半导体全电三电子双量子点量子比特（即混合量子比特）中模拟了随机基准测试协议。通过在感兴趣的门之间包含克利福德门的交错随机基准测试来提取特定量子计算门的平均误差。它提供了保真度的估计以及被测门平均误差的理论界限。
